New insight towards development of paclitaxel and docetaxel resistance in cancer cells: EMT as a novel molecular mechanism and therapeutic possibilities.

A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study.

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.

https://www.mdpi.com/2075-4418/11/5/756/pdf

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

A subset of skull base meningiomas (SBM) may show early progression/recurrence (P/R) as a result of incomplete resection. The purpose of this study is the implementation of MR radiomics to predict P/R in SBM. From October 2006 to December 2017, 60 patients diagnosed with pathologically confirmed SBM (WHO grade I, 56; grade II, 3; grade III, 1) were included in this study. Preoperative MRI including T2WI, diffusion-weighted imaging (DWI), and contrast-enhanced T1WI were analyzed. On each imaging modality, 13 histogram parameters and 20 textural gray level co-occurrence matrix (GLCM) features were extracted. Random forest algorithms were utilized to evaluate the importance of these parameters, and the most significant three parameters were selected to build a decision tree for prediction of P/R in SBM. Furthermore, ADC values obtained from manually placed ROI in tumor were also used to predict P/R in SBM for comparison. Gross-total resection (Simpson Grades I–III) was performed in 33 (33/60, 55%) patients, and 27 patients received subtotal resection. Twenty-one patients had P/R (21/60, 35%) after a postoperative follow-up period of at least 12 months. The three most significant parameters included in the final radiomics model were T1 max probability, T1 cluster shade, and ADC correlation. In the radiomics model, the accuracy for prediction of P/R was 90%; by comparison, the accuracy was 83% using ADC values measured from manually placed tumor ROI. The results show that the radiomics approach in preoperative MRI offer objective and valuable clinical information for treatment planning in SBM.

Radiomics approach for prediction of recurrence in skull base meningiomas

Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading.

Differentiation Researches on the Meningioma Subtypes by Radiomics From Contrast-Enhanced Magnetic Resonance Imaging: A Preliminary Study

Grey matter changes in patients with vestibular migraine.

In the clinical management of patients with locally advanced rectal cancer (LARC), the early identification of poor and good responders after neoadjuvant chemoradiotherapy (N-CRT) is essential. Therefore, we developed and validated predictive models including MRI findings from the structured report template, clinical and radiomics parameters to differentiate between poor and good responders in patients with locally advanced rectal cancer who underwent neoadjuvant chemoradiotherapy. Preoperative multiparametric MRI from 183 patients with locally advanced rectal cancer (122 in the training cohort, 61 in the validation cohort) was included in this retrospective study. After preprocessing, radiomic features were extracted and two methods of feature selection was applied to reduce the number of radiomics features. Logistic regression (LR) and random forest (RF) machine learning classifiers were trained to identify good responders from poor responders. Multivariable logistic regression analysis was used to incorporate the radiomic signature and clinical risk factors into a nomogram. Classifier performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). For the differentiation of poor and good responders, the radiomics model with an LR classifier achieved AUCs of 0.869 and 0.842 for the training and validation cohorts, respectively. The nomogram showed the highest reproducibility and prognostic ability in the training and validation cohorts, with AUCs of 0.923 (95% confidence interval, 0.872–0.975) and 0.898 (0.819–0.978), respectively. Additionally, the nomogram achieved significant risk stratification of patients in respect to progression free survival (PFS). The nomogram accurately differentiated good and poor responders in patients with LARC undergoing N-CRT, and showed significant performance for predicting PFS.

Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy.

Two adult patients diagnosed with Leukoencephalopathy with cerebral calcifications and cysts (LCC) were presented. Both patients had a long-term (8–10 years) following-up. Radiological findings of both patients revealed the characteristic signs of LCC: cerebral white matter abnormalities, calcifications, and cysts. In case 1, the initial CT scan showed a low-density area in the right frontal lobe and it had developed into a large cystic lesion after 8 years. Histopathological determination revealed that the cyst wall was associated with hemorrhage, angiomatous formation, and some Rosenthal fibers. In case 2, a major cystic lesion was located at the left parietal lobe which was resected and an old hematoma was found inside the cyst. Nine years later, the follow-up neuroimaging of case 2 showed a remarkable improvement of white matter abnormalities and cystic lesions. Hemorrhagic fluid was observed inside the cysts. Additionally, follow-up CT and MR scans showed a rapid enlargement of cystic lesions accompanied with hemorrhagic fluid levels after a year. Then, a major cyst was surgically removed to relieve pressure symptoms. Pathology of the resected cyst exhibited an organized hemorrhage inside the cyst and a large amount of hemosiderin surrounding the cyst wall. In conclusion, our two cases demonstrated that angiomatous changes subsequent with hemorrhage may be the major mechanism of cyst formation and development.

Leukoencephalopathy with cerebral calcifications and cysts: clinical and pathological features in two adults

A 29-year-old male with a 4-month history of binocular temporal visual field defect, 1-month history of memory loss and 5-day history of headache was admitted to our hospital. Brain MRI showed progressive signal abnormalities in the brain parenchyma. His laboratory tests showed elevated serum IgG4 and liver dysfunction. Abdominal CT, ultrasound and liver biopsy showed chronic liver disease. According to the diagnostic criteria of IgG4-related disease, IgG4-related inflammatory pseudotumor of brain parenchyma was considered. After methylprednisolone treatment, his symptoms improved and brain lesion reduced. This is the second reported case of a tumefactive lesion of the brain with serum IgG4 elevation, which was responsive to steroid treatment. Coincidentally, his mother had similar medical history and imaging findings, and was subspecialty diagnosed with the same disease, but without pathological and immunohistochemical confirmation. This suggests that the disease may be hereditary. It is important to recognize IgG4-related inflammatory pseudotumors of the brain parenchyma so that patients do not undergo unnecessary surgical or other procedures.

Xuejun Liu

Papers

Differentiation Researches on the Meningioma Subtypes by Radiomics From Contrast-Enhanced Magnetic Resonance Imaging: A Preliminary Study

Grey matter changes in patients with vestibular migraine.

Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy.

Leukoencephalopathy with cerebral calcifications and cysts: clinical and pathological features in two adults

IgG4-related inflammatory pseudotumor of the brain parenchyma: a case report and literature review